Abstract
Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.
| Original language | English |
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| Title of host publication | Proceedings of the twenty-eighth AAAI Conference on Artificial Intelligence and the twenty-sixth Innovative Applications of Artificial Intelligence Conference |
| Place of Publication | Palo Alto |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| Pages | 37-43 |
| Number of pages | 7 |
| ISBN (Print) | 9781577356615 , 9781577356776, 9781577356783, 9781577356790, 9781577356806 |
| Publication status | Published - Aug 2014 |
| Externally published | Yes |
| Event | AAAI Conference on Artificial Intelligence Twenty-Eighth AAAI Conference on Artificial Intelligence - Duration: 27 Jul 2014 → 31 Jul 2014 |
Publication series
| Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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| Publisher | AAAI Press |
| ISSN (Print) | 2159-5399 |
Conference
| Conference | AAAI Conference on Artificial Intelligence Twenty-Eighth AAAI Conference on Artificial Intelligence |
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| Abbreviated title | AAAI |
| Period | 27/07/14 → 31/07/14 |